Trauma and post-traumatic stress disorder in patients treated for opioid use disorder: findings from a 12-month cohort study
Bibliographic record
Abstract
Background Exposure to traumatic events is both a risk factor for substance use and an adverse outcome of substance use disorders. Identifying and managing post-traumatic stress disorder (PTSD) in patients with addiction requires attention. Aims To examine the lifetime prevalence of traumatic events and past-month prevalence of PSTD in patients treated for opioid use disorder, and explore the association between trauma, PTSD and treatment outcomes. Method Participants ( n = 674) receiving methadone treatment in 20 community clinics across Ontario, Canada, were administered the Mini-International Neuropsychiatric Interview to identify self-reported traumatic events and PTSD. Drug use was measured for 12 months by urine drug screens. Results Eleven per cent of participants met past-month criteria for PTSD ( n = 72), and 48% reported history of traumatic events with no current PTSD ( n = 323). Participants with PTSD were more likely to be female (odds ratio 2.13, 95% CI 1.20–3.76) and less likely to be employed (odds ratio 0.31, 95% CI 0.16–0.61) or married (odds ratio 0.51, 95% CI 0.26–0.90) than those with no trauma history. Antidepressants (39 v . 24%) and benzodiazepines (36 v . 18%) were differentially prescribed to patients with and without PTSD. Length of time in treatment and opioid use were not associated with trauma; however, suicidal ideation was more common in PTSD (odds ratio 2.29, 95% CI 1.04–5.01). Conclusions Trauma and PTSD are prevalent among patients with opioid use disorder, and consideration of trauma symptoms and associated characteristics is warranted. Patients with and without comorbid PTSD differ clinically and psychosocially, highlighting the relevance of integrating addiction and mental health services for this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".